Assessment of multiple land use functions promotes both utilization efficiency of land and regional coordination. Different personal and public products and services are offered by various land use types, meaning their functionality varies. Lack of judgment on temporal trends, turning points, or consideration of multi-source indicators like the ecological and air quality index leads to uncertainties in urban multifunctionality evaluation and functional orientation. In this study, the production-living-ecology land use function index system and evaluation process was improved using an entropy weight, triangle model, and coupling coordination degree. The production-living-ecology land use function (PLELUF) is defined from land use multi-functions. The Beijing-Tianjin-Hebei urban agglomeration was the representative area. The model was applied to quantify land use functions and measure spatio-temporal coordination and conflict from 1990 to 2015. Results found that the production and living functions displayed an overall upward trend and the growth rate of production function is larger, doubling from 1990 to 1995, while living function increases steadily. Ecology function remained steady from 1990 to 2000 but increased afterward. Land use function stage became balanced in ecology-living-production after 2005. No function-balanced cities existed in 1990; nine function-balanced cities were found in 2015. The coupling coordination degree increased from a slight conflict to a high coordination. Land use multi-functionality was high in the north and low in the south in 2015; Beijing had the most significant multifunctionality. This study can aid land use zoning and sustainable land management.

Abstract:Field net primary productivity (NPP) is useful in research modeling of regional and global carbon cycles and for validating results by remote sensing or process-based models. In this study, we used multiple models of NPP estimation and vegetation classification methods to study Chinese vegetation NPP characteristics, trends, and drivers using 7618 field measurements from the 1960s, 1980s, and 2000s. The values of other relevant NPP models, as well as process-based simulation and remote sensing models, were compared. Our results showed that NPP ranged from 3 to 12,407 gC·m −2 ·year −1 with a mean value of 571 gC·m −2 ·year −1 . Vegetation NPP gradually decreased from the southeast to the northwest. Forest, farmland, and grassland NPP was 1152, 294, and 518 gC·m −2 ·year −1 , respectively. Total NPP of grassland was higher than that of farmland. Total terrestrial NPP decreased from 3.58 to 3.41 Pg C·year −1 from the 1960s to the 2000s, a decadal decrease of 4.7%. Total NPP in forests and grasslands consistently showed a decreasing trend and decreased by 0.46 Pg C·year −1 and 0.16 Pg C·year −1 , respectively, whereas NPP for farmland showed an opposite trend, with a growth of 0.45 Pg C·year −1 . Our research findings filled gaps in the information regarding NPP for the entire landmass of China based on field data from a long-term time series and provide valuable information and a basis for validation analyses by remote sensing models, as well as a robust quantification of carbon estimation to anticipate future development at the national and global scale.

Pollution from potentially toxic trace elements (PTEs) is becoming serious and widespread in farmland soils in China, threatening food security and human health. Few large-scale studies systematically analyzed their temporal-spatial trends over vast spatially elaborate sites. The soil health status of the main grain producing areas was first announced based on a total of 3662 spatially elaborate farmland topsoil sites from the 1980s to the 2000s. Nearly 21.5% of sites were polluted, although only slightly. Pollution from the Cd, Ni, Cu, Zn, and Hg was more serious. Pollution was more extensive in the south than in the north. There was an increasing trend in the PTE concentrations, especially Cd with a growth of 21–25%, and in the proportion of mixed pollution at the sites (19.3%), Cd (21.5%), Pb (3.6%), Zn (5.7%), Cu (7.0%), and Hg (3.1%). Furthermore, temporal variations in severe Cd pollution and mixed-level Hg pollution in the north are severer. This study may provide guidance for policymakers regarding the protection and high-risk area of PTE contamination in the soils.

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High-resolution (HR) remote sensing images have important applications in many scenarios, and improving the resolution of remote sensing images via algorithms is one of the key research fields. However, current super-resolution (SR) algorithms, which are trained on synthetic datasets, tend to have poor performance in real-world low-resolution (LR) images. Moreover, due to the inherent complexity of real-world remote sensing images, current models are prone to color distortion, blurred edges, and unrealistic artifacts. To address these issues, real-SR datasets using the Gao Fen (GF) satellite images at different spatial resolutions have been established to simulate real degradation situations; moreover, a second-order attention generator adversarial attention network (SA-GAN) model based on real-world remote sensing images is proposed to implement the SR task. In the generator network, a second-order channel attention mechanism and a region-level non-local module are used to fully utilize the a priori information in low-resolution (LR) images, as well as adopting region-aware loss to suppress artifact generation. Experiments on test data demonstrate that the model delivers good performance for quantitative metrics, and the visual quality outperforms that of previous approaches. The Frechet inception distance score (FID) and the learned perceptual image patch similarity (LPIPS) value using the proposed method are improved by 17.67% and 6.61%, respectively. Migration experiments in real scenarios also demonstrate the effectiveness and robustness of the method.

The sea ice cover is changing rapidly in polar regions, and sea ice products with high temporal and spatial resolution are of great importance in studying global climate change and navigation. In this paper, an ice map generation model based on Moderate-Resolution Imaging Spectroradiometer (MODIS) reflectance bands is constructed to obtain sea ice data with a high temporal and spatial resolution. By constructing a training sample library and using a multi-feature fusion machine learning algorithm for model classification, the high-accuracy recognition of ice and cloud regions is achieved. The first product provided by this algorithm is a near real-time single-scene sea ice presence map. Compared with the photo-interpreted ground truth, the verification shows that the algorithm can obtain a higher recognition accuracy for ice, clouds, and water, and the accuracy exceeds 98%. The second product is a daily and weekly clear sky map, which provides synthetic ice presence maps for one day or seven consecutive days. A filtering method based on cloud motion is used to make the product more accurate. The third product is a weekly fusion of clear sky optical images. In a comparison with the Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration products performed in August 2019 and September 2020, these composite images showed spatial consistency over time, suggesting that they can be used in many scientific and practical applications in the future.

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Clouds play an important role in the energy and moisture cycle of the earth–atmosphere system, which affects many important processes in nature and human societies. However, there are very few fine-grained and high-precision global cloud climatology data available for high-resolution models. In this paper, we produced a fine-grained (1 km resolution) global land cloud climatology (GLHCC) report based on MOD09 cloud masks from 2001 to 2016, with a temporal resolution of 10 days. The two improvements (short-wave infrared and Band 2/6 ratio threshold method) on the original MOD09 cloud mask have reduced the snow, ice, and bright areas mistakenly classified as clouds. The preliminary cloud products undergo the removal of orbital artifacts by Variational Stationary Noise Remover (VSNR) and the removal of abnormal albedo areas to generate the final cloud climatology data. The new product was directly validated by ground-based cloud observations collected from 3777 global weather stations. PATMOS-X from the Advanced Very High Resolution Radiometer (AVHRR) and MOD/MYD35 served as comparison products for consistency check of GLHCC. The assessment results show that GLHCC demonstrated a strong correlation with ground station observations, MOD/MYD35, and PATMOS-X. When the ground observations were taken as the truth value, GLHCC and MOD/MYD35 displayed higher accuracy than PATMOS-X. In most selected interested areas where the three behave differently, GLHCC matched the facts better than MOD/MYD35 and PATMOS-X. The GLHCC can well represent the cloud distribution over the past 16 years and will play an important role in the fine-grained demands of many aspects of nature and human society.